A Novel Hierarchical Entropy-Based Framework for Ransomware Detection in Encrypted Network Traffic

Encrypted network traffic has increasingly complicated efforts to detect malicious activities, necessitating innovative approaches capable of identifying anomalies without relying on content inspection. A hierarchical entropy-based framework has been developed to address the challenge of detecting ransomware in encrypted communications, leveraging statistical measures of unpredictability to differentiate benign and malicious traffic. Entropy calculations were employed at multiple analytical layers, dynamically adjusting thresholds to reflect variations in network behavior and enhancing detection accuracy across diverse ransomware families. Unlike traditional methods, which often depend on known signatures or heuristics, the proposed framework focuses on inherent properties of data streams, enabling effective identification of novel ransomware variants. Experimental results demonstrated consistently high detection rates, with low false positive and negative occurrences, showing the framework’s robustness in real-world conditions. A notable advantage of this approach lies in its ability to preserve privacy by analyzing statistical patterns rather than decrypting sensitive content. Scalability was confirmed through evaluations under varying traffic loads, where processing efficiency remained stable even at peak conditions. The integration of temporal and spatial entropy metrics allowed for a comprehensive examination of traffic flows, further improving the precision of anomaly detection. An analysis of protocol-specific behaviors highlighted differences in ransomware communication patterns, offering insights that informed the refinement of detection mechanisms. Command-and-control traffic was effectively identified through entropy measures, showcasing the framework’s capability to detect covert operations. The proposed solution provides a significant advancement in cybersecurity defense, addressing gaps in existing methodologies and demonstrating its potential as a reliable tool for real-time ransomware detection in encrypted environments.